Chaotic Characteristics Analysis on Aerostat RCS via Phase Space Reconstruction and Largest Lyapunov Exponent

Abstract

This paper qualitatively and quantitatively analyzes the chaotic characteristics of radar cross section (RCS) time series concerning an aerostat. First, the phase space of aerostat RCS time series is reconstructed via the time delay processing. And the corresponding optimal delay time as well as the optimal embedding dimension are determined with the C-C method. Then, the small data sets method is utilized to get the largest Lyapunov exponent of aerostat RCS time series. Finally, a case study on aerostat RCS time series is carried out. The results show that regular chaotic attractors exist in the reconstructed phase space, and the calculated largest Lyapunov exponent is larger than zero, thus demonstrating that the aero tat RCS time series has chaotic characteristics.